1 Processed data

This is an interactive table of the covariate data.

2 Normalisation quality control metrics

2.1 Principal component analysis

The principal component analysis plot shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design.

In presence of strong biological signal, the samples should cluster with the biological condition. When samples are clustered according to other effects (for example patient, or technical batch), great care must be used when interpreting the results, as the other effects will considerably reduce the ability to extract meaningful biological information.

Download plot

2.2 Hierarchical clustering

The hierarchical clustering shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

The hierarchical clustering can provide clues on which groups of genes could affect the clustering of samples.

Download plot

2.3 Sample similarity

The hierarchical clustering shown below was generated using all the full normalised dataset (21264 genes). The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

Download plot

2.4 Normalised expression densities

The expression values are obtained by the “vst” method, where the experimental design has been used for normalisation.

Download plot

2.5 DESeq2 normalisation

## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.

## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.

Download plot

2.6 Cox outliers

## Warning: Removed 62780 rows containing non-finite values (stat_boxplot).

## Warning: Removed 62780 rows containing non-finite values (stat_boxplot).

Download plot

3 Contrasts

Contrasts generated by the pipeline.

3.1 NHBE_SC2V

3.1.1 MA plot

A MA plot of the contrast NHBE_SC2V.

3.1.2 Results table

An interactive data table of the contrast results for NHBE_SC2V.

3.1.3 tmod enrichment analysis for NHBE_SC2V

Table. Summary of the results for contrast NHBE_SC2V shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
msigdb_reactome 178 127 71 27
msigdb_hallmark 29 27 25 21
msigdb_mir 12 6 3 0
msigdb_go_bp 301 126 64 23
tmod 69 47 29 16

Table. Results of the tmod enrichment analysis for contrast NHBE_SC2V. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval
msigdb_hallmark.pval
msigdb_mir.pval
msigdb_go_bp.pval
tmod.pval

3.1.4 cluster profiler results

3.1.4.1 Dot plot

Dot plot for cluster profiler results for contrast NHBE_SC2V.

MSigDb
H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO
BP

MF

KEGG
pathways

3.1.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast NHBE_SC2V.

MSigDb
H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO
BP

MF

KEGG
pathways

3.1.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast NHBE_SC2V.

MSigDb
H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
BP

MF

KEGG
pathways

3.2 A549_SC2V

3.2.1 MA plot

A MA plot of the contrast A549_SC2V.

3.2.2 Results table

An interactive data table of the contrast results for A549_SC2V.

3.2.3 tmod enrichment analysis for A549_SC2V

Table. Summary of the results for contrast A549_SC2V shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
msigdb_reactome 172 96 53 25
msigdb_hallmark 18 18 18 14
msigdb_mir 18 12 3 1
msigdb_go_bp 240 143 83 35
tmod 46 29 22 10

Table. Results of the tmod enrichment analysis for contrast A549_SC2V. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval
msigdb_hallmark.pval
msigdb_mir.pval
msigdb_go_bp.pval
tmod.pval

3.2.4 cluster profiler results

3.2.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_SC2V.

MSigDb
H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO
BP

MF

KEGG
pathways

3.2.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_SC2V.

MSigDb
H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO
BP

MF

KEGG
pathways

3.2.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_SC2V.

MSigDb
H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
BP

MF

KEGG
pathways

3.3 A549_RSV

3.3.1 MA plot

A MA plot of the contrast A549_RSV.

3.3.2 Results table

An interactive data table of the contrast results for A549_RSV.

3.3.3 tmod enrichment analysis for A549_RSV

Table. Summary of the results for contrast A549_RSV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
msigdb_reactome 255 154 99 49
msigdb_hallmark 32 32 31 27
msigdb_mir 23 16 14 6
msigdb_go_bp 451 246 165 90
tmod 85 48 32 21

Table. Results of the tmod enrichment analysis for contrast A549_RSV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval
msigdb_hallmark.pval
msigdb_mir.pval
msigdb_go_bp.pval
tmod.pval

3.3.4 cluster profiler results

3.3.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_RSV.

MSigDb
H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO
BP

MF

KEGG
pathways

3.3.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_RSV.

MSigDb
H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO
BP

MF

KEGG
pathways

3.3.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_RSV.

MSigDb
H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
BP

MF

KEGG
pathways

3.4 A549_IAV

3.4.1 MA plot

A MA plot of the contrast A549_IAV.

3.4.2 Results table

An interactive data table of the contrast results for A549_IAV.

3.4.3 tmod enrichment analysis for A549_IAV

Table. Summary of the results for contrast A549_IAV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
msigdb_reactome 235 145 81 36
msigdb_hallmark 25 24 22 18
msigdb_mir 39 33 27 16
msigdb_go_bp 385 231 175 92
tmod 63 38 23 11

Table. Results of the tmod enrichment analysis for contrast A549_IAV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval
msigdb_hallmark.pval
msigdb_mir.pval
msigdb_go_bp.pval
tmod.pval

3.4.4 cluster profiler results

3.4.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_IAV.

MSigDb
H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO
BP

MF

KEGG
pathways

3.4.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_IAV.

MSigDb
H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO
BP

MF

KEGG
pathways

3.4.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_IAV.

MSigDb
H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
BP

MF

KEGG
pathways

3.5 A549_SC2V_vs_IAV

3.5.1 MA plot

A MA plot of the contrast A549_SC2V_vs_IAV.

3.5.2 Results table

An interactive data table of the contrast results for A549_SC2V_vs_IAV.

## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html

3.5.3 tmod enrichment analysis for A549_SC2V_vs_IAV

Table. Summary of the results for contrast A549_SC2V_vs_IAV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
msigdb_reactome 197 100 66 23
msigdb_hallmark 22 20 20 18
msigdb_mir 83 72 63 45
msigdb_go_bp 430 239 172 88
tmod 75 50 22 6

Table. Results of the tmod enrichment analysis for contrast A549_SC2V_vs_IAV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval
msigdb_hallmark.pval
msigdb_mir.pval
msigdb_go_bp.pval
tmod.pval

3.5.4 cluster profiler results

3.5.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_SC2V_vs_IAV.

MSigDb
H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO
BP

MF

KEGG
pathways

3.5.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_SC2V_vs_IAV.

MSigDb
H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO
BP

MF

KEGG
pathways

3.5.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_SC2V_vs_IAV.

MSigDb
H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
BP

MF

KEGG
pathways

3.6 A549_SC2V_vs_RSV

3.6.1 MA plot

A MA plot of the contrast A549_SC2V_vs_RSV.

3.6.2 Results table

An interactive data table of the contrast results for A549_SC2V_vs_RSV.

## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html

3.6.3 tmod enrichment analysis for A549_SC2V_vs_RSV

Table. Summary of the results for contrast A549_SC2V_vs_RSV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB 0.01 0.001 1e-04 1e-06
msigdb_reactome 342 230 147 81
msigdb_hallmark 32 31 29 27
msigdb_mir 54 47 38 24
msigdb_go_bp 679 439 300 188
tmod 116 79 56 29

Table. Results of the tmod enrichment analysis for contrast A549_SC2V_vs_RSV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval
msigdb_hallmark.pval
msigdb_mir.pval
msigdb_go_bp.pval
tmod.pval

3.6.4 cluster profiler results

3.6.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_SC2V_vs_RSV.

MSigDb
H

C2

GO
BP

MF

KEGG
pathways

3.6.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_SC2V_vs_RSV.

MSigDb
H

C2

GO
BP

MF

KEGG
pathways

3.6.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_SC2V_vs_RSV.

MSigDb
H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
BP

MF

KEGG
pathways

4 Functional analysis

4.1 Gene set enrichment analysis with tmod

4.1.1 Overview

Table. Overview of the databases for which gene set enrichment using tmod was performed.

ID Name Description TaxonID N
msigdb_reactome Reactome gene sets (MSigDB) Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 1499
msigdb_hallmark Hallmark gene sets (MSigDB) Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 50
msigdb_mir MIR targets (MSigDB) MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 221
msigdb_go_bp GO Biological Process (MSigDB) GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 7350
tmod Co-expression gene sets (tmod) Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information. 9606 606

4.2 tmod enrichment analysis results for database Reactome gene sets (MSigDB).

4.2.1 Summary

Database ID: msigdb_reactome.

Description: Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
NHBE_SC2V_ID0.pval 453 299 204 84
A549_SC2V_ID1.pval 399 257 140 59
A549_RSV_ID2.pval 586 354 215 91
A549_IAV_ID3.pval 493 310 190 63
A549_SC2V_vs_IAV_ID4.pval 505 287 157 67
A549_SC2V_vs_RSV_ID5.pval 645 444 280 138
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

4.2.2 Figure

Fig. Panel plot showing results for the database msigdb_reactome.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

4.2.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

4.3 tmod enrichment analysis results for database Hallmark gene sets (MSigDB).

4.3.1 Summary

Database ID: msigdb_hallmark.

Description: Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
NHBE_SC2V_ID0.pval 48 46 39 30
A549_SC2V_ID1.pval 41 37 35 25
A549_RSV_ID2.pval 46 44 41 36
A549_IAV_ID3.pval 42 37 32 20
A549_SC2V_vs_IAV_ID4.pval 46 44 38 29
A549_SC2V_vs_RSV_ID5.pval 46 45 42 35

4.3.2 Figure

Fig. Panel plot showing results for the database msigdb_hallmark.

4.3.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

4.4 tmod enrichment analysis results for database MIR targets (MSigDB).

4.4.1 Summary

Database ID: msigdb_mir.

Description: MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
NHBE_SC2V_ID0.pval 160 123 75 28
A549_SC2V_ID1.pval 172 138 89 32
A549_RSV_ID2.pval 177 150 93 51
A549_IAV_ID3.pval 178 145 108 49
A549_SC2V_vs_IAV_ID4.pval 191 164 133 84
A549_SC2V_vs_RSV_ID5.pval 196 151 109 71

4.4.2 Figure

Fig. Panel plot showing results for the database msigdb_mir.

4.4.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

4.5 tmod enrichment analysis results for database GO Biological Process (MSigDB).

4.5.1 Summary

Database ID: msigdb_go_bp.

Description: GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
NHBE_SC2V_ID0.pval 2262 1434 913 510
A549_SC2V_ID1.pval 1434 887 583 297
A549_RSV_ID2.pval 2314 1461 962 569
A549_IAV_ID3.pval 1546 1013 653 350
A549_SC2V_vs_IAV_ID4.pval 1871 1212 793 440
A549_SC2V_vs_RSV_ID5.pval 2387 1588 1083 618

4.5.2 Figure

Fig. Panel plot showing results for the database msigdb_go_bp.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

4.5.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

4.6 tmod enrichment analysis results for database Co-expression gene sets (tmod).

4.6.1 Summary

Database ID: tmod.

Description: Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
NHBE_SC2V_ID0.pval 159 95 62 21
A549_SC2V_ID1.pval 134 59 34 14
A549_RSV_ID2.pval 183 105 54 23
A549_IAV_ID3.pval 138 75 41 15
A549_SC2V_vs_IAV_ID4.pval 168 90 54 10
A549_SC2V_vs_RSV_ID5.pval 203 131 82 44

4.6.2 Figure

Fig. Panel plot showing results for the database tmod.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

4.6.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

4.7 Cluster profiler summary overview by database

4.7.1 Overview

Table. Overview of the databases for which gene set enrichment using cluster_profiler was performed.

4.7.2 MSigDb.H

Fig. Panel plot showing results for the database MSigDb.H. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.

4.7.3 MSigDb.C2

Fig. Panel plot showing results for the database MSigDb.C2. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.

4.7.4 GO.BP

Fig. Panel plot showing results for the database GO.BP. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

4.7.5 GO.MF

Fig. Panel plot showing results for the database GO.MF. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

4.7.6 KEGG.pathways

Fig. Panel plot showing results for the database KEGG.pathways. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

5 Session Info

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /fast/work/users/jweiner_m/miniconda3/envs/sea_snap/lib/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] orthomapper_0.0.0.9000      tmod_0.43                   enrichplot_1.2.0            glue_1.3.1                  pander_0.6.3               
##  [6] forcats_0.4.0               stringr_1.4.0               dplyr_0.8.3                 purrr_0.3.3                 readr_1.3.1                
## [11] tidyr_1.0.0                 tibble_2.1.3                ggplot2_3.2.1               tidyverse_1.3.0             magrittr_1.5               
## [16] DT_0.11                     yaml_2.2.0                  DESeq2_1.22.1               SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
## [21] BiocParallel_1.16.6         matrixStats_0.55.0          Biobase_2.42.0              GenomicRanges_1.34.0        GenomeInfoDb_1.18.1        
## [26] IRanges_2.16.0              S4Vectors_0.20.1            BiocGenerics_0.28.0         colorout_1.2-2             
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           backports_1.1.5        fastmatch_1.1-0        Hmisc_4.2-0            plyr_1.8.4             igraph_1.2.4.2         lazyeval_0.2.2        
##   [8] splines_3.5.1          crosstalk_1.0.0        urltools_1.7.3         digest_0.6.23          plotwidgets_0.4        htmltools_0.4.0        GOSemSim_2.8.0        
##  [15] viridis_0.5.1          GO.db_3.7.0            checkmate_1.9.4        memoise_1.1.0          cluster_2.1.0          limma_3.38.3           graphlayouts_0.5.0    
##  [22] annotate_1.60.1        modelr_0.1.5           prettyunits_1.0.2      colorspace_1.4-1       ggrepel_0.8.1          blob_1.2.0             rvest_0.3.5           
##  [29] haven_2.2.0            xfun_0.11              tagcloud_0.6           crayon_1.3.4           RCurl_1.95-4.12        jsonlite_1.6           genefilter_1.64.0     
##  [36] zeallot_0.1.0          survival_2.44-1.1      polyclip_1.10-0        gtable_0.3.0           zlibbioc_1.28.0        XVector_0.22.0         UpSetR_1.4.0          
##  [43] DOSE_3.8.2             scales_1.1.0           pheatmap_1.0.12        vsn_3.50.0             DBI_1.0.0              Rcpp_1.0.3             progress_1.2.2        
##  [50] viridisLite_0.3.0      xtable_1.8-4           htmlTable_1.13.2       gridGraphics_0.4-1     europepmc_0.3          foreign_0.8-72         bit_1.1-14            
##  [57] preprocessCore_1.44.0  Formula_1.2-3          htmlwidgets_1.5.1      httr_1.4.1             fgsea_1.8.0            RColorBrewer_1.1-2     acepack_1.4.1         
##  [64] ellipsis_0.3.0         pkgconfig_2.0.3        XML_3.98-1.20          farver_2.0.1           nnet_7.3-12            dbplyr_1.4.2           locfit_1.5-9.1        
##  [71] reshape2_1.4.3         ggplotify_0.0.4        tidyselect_0.2.5       labeling_0.3           rlang_0.4.2            later_1.0.0            AnnotationDbi_1.44.0  
##  [78] munsell_0.5.0          cellranger_1.1.0       tools_3.5.1            cli_1.1.0              generics_0.0.2         RSQLite_2.1.2          ggridges_0.5.2        
##  [85] broom_0.5.3            evaluate_0.14          fastmap_1.0.1          knitr_1.26             bit64_0.9-7            fs_1.3.1               tidygraph_1.1.2       
##  [92] ggraph_2.0.0           nlme_3.1-141           mime_0.7               DO.db_2.9              xml2_1.2.2             compiler_3.5.1         rstudioapi_0.10       
##  [99] beeswarm_0.2.3         affyio_1.52.0          reprex_0.3.0           tweenr_1.0.1           geneplotter_1.60.0     stringi_1.4.3          lattice_0.20-38       
## [106] Matrix_1.2-17          vctrs_0.2.0            pillar_1.4.2           lifecycle_0.1.0        BiocManager_1.30.10    triebeard_0.3.0        cowplot_1.0.0         
## [113] data.table_1.11.6      bitops_1.0-6           qvalue_2.14.1          httpuv_1.5.2           R6_2.4.1               latticeExtra_0.6-28    affy_1.60.0           
## [120] promises_1.1.0         gridExtra_2.3          MASS_7.3-51.4          assertthat_0.2.1       withr_2.1.2            GenomeInfoDbData_1.2.1 hms_0.5.2             
## [127] grid_3.5.1             rpart_4.1-15           rmarkdown_2.0          rvcheck_0.1.7          Cairo_1.5-10           ggforce_0.3.1          shiny_1.4.0           
## [134] lubridate_1.7.4        base64enc_0.1-3